Bearing fault diagnosis under different operating conditions based on source domain multi sample joint distribution adaptation

Li, Xuezeng; Zhang, Tingfeng

doi:10.1109/ccdc55256.2022.10034076

Cited by 1 publication

(1 citation statement)

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“…In this network, the feature transfer using TCA and a pretrained convolutional neural network is performed. In [23], a source domain multisample JDA (SM-JDA) approach was used for the bearing fault diagnosis under variable operating conditions. In [24], the BDA was introduced to facilitate the domain adaptation on bearing cross-domain fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A New Framework Based on Supervised Joint Distribution Adaptation for Bearing Fault Diagnosis across Diverse Working Conditions

Liu,

Dong

2024

Shock and Vibration

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To address the degradation of diagnostic performance due to data distribution differences and the scarcity of labeled fault data, this study has focused on transfer learning-based cross-domain fault diagnosis, which attracts considerable attention. However, deep transfer learning-based methods often present a challenge due to their time-consuming and costly nature, particularly in tuning hyperparameters. For this issue, on the basis of classical features-based transfer learning method, this study introduces a new framework for bearing fault diagnosis based on supervised joint distribution adaptation and feature refinement. It first utilizes ensemble empirical mode decomposition to process raw signals, and statistical features extraction is implemented. Then, a new feature refinement module is designed to refine domain adaptation features from high-dimensional feature set by evaluating the fault distinguishability and working-condition invariance of feature data. Next, it proposes a supervised joint distribution adaptation method to conduct improved joint distribution alignment that preserves neighborhood relationships within a manifold subspace. Finally, an adaptive classifier is trained to predict fault labels of feature data across varying working conditions. To prove the cross-domain fault diagnosis performance and superiority of the proposed methods, two bearing datasets are applied for experiments, and the experimental results verify that the model built by the proposed framework can achieve desirable diagnosis performance under different working conditions and that it apparently outperforms comparative models.

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Section: Introductionmentioning

confidence: 99%